A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil

Abstract

This data-driven work aims to analyze and classify the spatiotemporal distribution of all Brazilian states considering data so diverse as the number of Covid-19 cases, deaths, confirmed cases per 100 k inhabitants, mortality per 100 k inhabitants and case fatality rates as health indicators. We also considered population, area and population density as geographic indicators. Finally, GDP and HDI were taken into account as economic and social criteria. For this task data were collected from April 3rd until August 8th, 2020, corresponding to epidemiological weeks 14–32, reaching three million cases and a hundred thousand deaths. With this data it was possible to classify Brazilian states using multivariate methods into possible groups by means of non-hierarchical ( k -means) cluster as well as factor analysis. It was possible to group all states plus the Federal District into five clusters, taking into account these 10 variables over the first five months of the epidemic. Group changes between states were observed over time and clusters, and between three and four factors were found. However, even with great difference on health indicators during days, the number of clusters remains fixed. Also, São Paulo and Rio de Janeiro states were ranked at top list taking into account all epidemiological weeks. Correlations were observed between variables, such as the number of Covid cases and deaths with GDP for most of epidemiological weeks. Some clusters were more critical due to specific variables, including cities that are main hotspots. These multivariate findings would provide a comprehensive description of the ongoing Covid-19 epidemic and may help to guide subsequent studies to understand and control virus transmission.

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“…On March 17, 2020, the first death from COVID-19 was reported. 43 On May 23, 2020, Brazil ranked third worldwide in the number of cases and deaths from COVID-19. A few weeks later, the country ranked second worldwide in the number of infections and fatal victims of the virus.…”

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“…Papers from India 10 11 Brazil 12 and Indonesia 13 assess number of confirmed cases and deaths on local scales. Nascimento 12 was the only study to use additional factors in their clustering, including population, land area, gross domestic product, population density and the Human Development Index. They performed clustering analysis on a weekly basis from 3 April to 8 August, while the other local level studies performed clustering using single day data.…”

Komparasi Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Penyebaran Covid-19 di Indonesia

“…Kasus virus ini ditemukan pertama kali di provinsi Wuhan dan beberapa gejala yang dialami apabila terinfeksi virus ini antara lain batuk, demam, letih, sesak nafas, dan mengalami penurunan nafsu makan [2]. Secara umum virus ini dapat menular melalui droplet atau cairan tubuh yang dikeluarkan selama bersin dan batuk [3][4][5]. Negara Indonesia adalah salah satu dari berbagai negara di dunia yang mengalami dampak COVID-19.…”